Product customization is attracting more attentions
in industry as a viable strategy to better meet customer
requirements and gain more profit. However the vast number
of product variants in product customization process often
makes it difficult for consumers to make purchase decisions,
a phenomenon referred to as information overload. In this
paper we take a two-prong approach to tackle the issue of
information overload in customized products recommendation.
Basically, the method answers two questions, namely,
which products to recommend and in what order to present
the recommendations. Firstly, a probability relevance
model is deployed to calculate the probability of relevance
for each end product. Then a probability ranking principle
is exploited to present the recommendations. The approach
also takes customer flexibility into consideration and thus
mitigates the effect of inconsistent specifications from customers.
It does not require any prior knowledge about an
active customer’s preference and can accommodate the new
customers challenge facing by recommendation approaches.
Analytical results show that the method is optimal in terms
of customer’s utility and product recommendation efficiency.
Numerical experiments are also conducted to test the presented
approach.